tubestation/toolkit/components/ml/docs/api.rst

APIs
====

Running the pipeline API
::::::::::::::::::::::::

You can use the Transformer.js `pipeline` API directly to perform inference, as long
as the model is in our model hub.

The `Transformers.js documentation <https://huggingface.co/tasks>`_ provides a lot
of examples that you can slightly adapt when running in Firefox.

In the example below, a text summarization task is performed using the `summarization` task:

.. code-block:: javascript

  const { createEngine } = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");
  const options = {
    taskName: "summarization",
    modelId: "mozilla/text_summarization",
    modelRevision: "main"
  };

  const engine = await createEngine(options);

  const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
  'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
  'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
  'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
  'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
  'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
  'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
  'tallest free-standing structure in France after the Millau Viaduct.';

  const request = { args:  [text], options: { max_new_tokens: 100 } };
  const res = await engine.run(request);
  console.log(res[0]["summary_text"]);


You can use the browser console or toolbox to run this example.
To enable the browser console, flip the following option in `about:config`: **devtools.chrome.enabled**.
To get access to the full toolbox, set the **devtools.debugger.remote-enabled** option.
We recommend using the toolbox to get access to more tools. You will get a security warning
when starting it, when the toolbox connects to the browser.

When running this code, Firefox will look for models in the Mozilla model hub located at https://model-hub.mozilla.org
which contains a curated list of models.

Available Options
:::::::::::::::::

Options passed to the `createEngine` function are verified and converted into a `PipelineOptions` object.

Below are the options available:

- **taskName**: The name of the task the pipeline is configured for.
- **engineId**:  The identifier for the engine to be used by the pipeline.
- **timeoutMS**: The maximum amount of time in milliseconds the pipeline should wait for a response.
- **modelHubRootUrl**: The root URL of the model hub where models are hosted.
- **modelHubUrlTemplate**: A template URL for building the full URL for the model.
- **modelId**: The identifier for the specific model to be used by the pipeline.
- **modelRevision**: The revision for the specific model to be used by the pipeline.
- **tokenizerId**: The identifier for the tokenizer associated with the model, used for pre-processing inputs.
- **tokenizerRevision**: The revision for the tokenizer associated with the model, used for pre-processing inputs.
- **processorId**: The identifier for any processor required by the model, used for additional input processing.
- **processorRevision**: The revision for any processor required by the model, used for additional input processing.
- **logLevel**: The log level used in the worker
- **runtimeFilename**: Name of the runtime wasm file.

**taskName** and **modelId** are required, the others are optional and will be filled automatically
using values pulled from Remote Settings when the task id is recognized.

To learn about the different inference tasks, refer to this Hugging Face
documentation: `Tasks <https://huggingface.co/tasks>`_

**engineId** is used to manage the lifecycle of the engine. When not provided, it defaults to
`default-engine`. Everytime a new engine is created using `createEngine` the API will ensure that
there's a single engine with the given id. If the options of the existing engine are not different,
the instance is reused. If they differ, the engine is reinitialized with the new options.
This ensures we don't have too many engines running at once since it takes a lot of resources.
To make sure your engine is not destroyed or reused elsewhere, set that value with a unique id
that matches your component.

Some values are also set from the preferences (set in `about:config`):

- **browser.ml.logLevel**: Set to "All" to see all logs, which are useful for debugging.
- **browser.ml.modelHubRootUrl**: Model hub root URL used to download models
- **browser.ml.modelHubUrlTemplate**: Model URL template
- **browser.ml.modelCacheTimeout**: Model cache timeout in ms
- **browser.ml.modelCacheMaxSizeBytes**: Maximum disk size for ML model cache (in bytes)


Using the Hugging Face model hub
::::::::::::::::::::::::::::::::

By default, the engine will use the Mozilla model hub and will error out if you try to use any other hub for security reasons.

If you want to use the Hugging Face model hub, you will need to run Firefox with the `MOZ_ALLOW_EXTERNAL_ML_HUB` environment variable
set to `1`, then set in `about:config` these two values:

- `browser.ml.modelHubRootUrl` to `https://huggingface.co`
- `browser.ml.modelHubUrlTemplate` to `{model}/resolve/{revision}`

The inference engine will then look for models in the Hugging Face model hub.


To run against a Hugging Face model, visit `this page <https://huggingface.co/models?library=transformers.js>`_ and select on
the top left corner `tasks`. You can pick a task and then choose a model.

For example, models for the `summarization` tasks compatible with our inference engine are listed `here <https://huggingface.co/models?pipeline_tag=summarization&library=transformers.js&sort=trending>`_.

Let's say you want to pick the `Xenova/distilbart-cnn-6-6` model. All you have to do is use the id when calling our
`createEngine` pipeline:

.. code-block:: javascript

  const { createEngine } = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");

  const options = {
    taskName: "summarization",
    modelId: "Xenova/distilbart-cnn-6-6",
  };

  const engine = await createEngine(options);

  const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
  'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
  'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
  'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
  'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
  'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
  'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
  'tallest free-standing structure in France after the Millau Viaduct.';

  const request = { args:  [text], options: { max_new_tokens: 100 } };
  const res = await engine.run(request);
  console.log(res[0]["summary_text"]);


Running the internal APIs
:::::::::::::::::::::::::

Some inference tasks are doing more complex operations within the engine, such as image processing.
For these tasks, you can use the internal APIs to run the inference. Those tasks are prefixed with `moz`.

In the example below, an image is converted to text using the `moz-image-to-text` task.


.. code-block:: javascript

  const { createEngine } = ChromeUtils.importESModule("chrome://global/content/ml/EngineProcess.sys.mjs");

  // options needed for the task
  const options = {taskName: "moz-image-to-text" };

  // We create the engine object, using the options
  const engine = await createEngine(options);

  // Preparing a request
  const request = {url: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg"};

  // At this point we are ready to do some inference.
  const res = await engine.run(request);

  // The result is a string containing the text extracted from the image
  console.log(res);


The following internal tasks are supported by the machine learning engine:

.. js:autofunction:: imageToText